ARTIFICIAL INTELLIGENCE

This study investigates the impact of Artificial Intelligence (AI) on audit efficiency within the private sector. The rapid advancement of AI technologies has transformed traditional auditing processes by enhancing data accuracy, speed, and decision-making. The objectives of this research are to examine the effect of AI on audit efficiency, evaluate the challenges auditors face in adopting AI-driven tools, ascertain the implications of AI integration on the future roles and skills required of auditors, and determine how AI supports auditors’ professional judgment and decision-making during audits. The study adopts a quantitative research approach through the administration of structured questionnaires to auditors in selected private organizations. The data collected were analysed using descriptive and inferential statistical tools. Findings reveal that the adoption of AI significantly improves audit efficiency by automating repetitive tasks, reducing human error, and enabling real-time data analysis. However, the study also identifies key challenges, including high implementation costs, lack of technical expertise, data security concerns, and resistance to technological change. Furthermore, the integration of AI necessitates the acquisition of advanced digital and analytical skills among auditors to remain relevant in the evolving audit environment. The study concludes that while AI serves as a strategic tool for improving audit quality and efficiency, adequate training and organizational support are essential for its effective implementation.

This study investigates the impact of Artificial Intelligence (AI) on entrepreneurship education among students of the University of Benin, Benin City, Nigeria. It focuses on how selected AI tools ChatGPT, Grammarly, Quillbot, and Meta AI affect students’ learning experiences, creativity, and innovative capacities within entrepreneurship courses. A cross sectional survey research design was adopted, and data were collected from one hundred (100) undergraduate students in the Department of ntrepreneurship through a structured questionnaire. Descriptive and inferential statistical techniques, including correlation and regression analyses, were employed to test the study’s hypotheses at a 5% level of significance. Findings revealed that AI tools significantly enhance entrepreneurship education by improving students’ understanding of business concepts, fostering innovative thinking, and strengthening communication and writing skills. However, challenges such as poor digital infrastructure, high data costs, limited awareness, and inadequate institutional support were identified as major barriers to effective AI integration. The study concludes that while AI technologies hold transformative potential for entrepreneurship education, their effective utilization requires strategic curriculum integration, digital capacity building, and infrastructural investment. It recommends that universities incorporate AI literacy into entrepreneurship programs, provide training for educators, and create enabling environments for students to explore AI tools responsibly and productively.

This study explores the extent to which AI-driven tools such as machine learning, natural language processing, and data analytics enhance auditors’ ability to detect anomalies, assess risks, and provide deeper insights into financial statements. AI’s capacity to process vast datasets in real time reduces human error, strengthens fraud detection, and enables auditors to focus on judgment-intensive tasks, thereby improving audit quality. Moreover, automation of repetitive audit procedures accelerates workflow, minimizes costs, and enhances overall efficiency. However, the adoption of AI also raises concerns about data security, auditor independence, ethical implications, and the need for continuous skill development. This paper argues that while AI does not replace professional skepticism and human judgment, it serves as a powerful enabler that reshapes auditing practices toward greater reliability, transparency, and efficiency. The findings contribute to ongoing debates on the future of auditing and provide practical insights for regulators, practitioners, and stakeholders.

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) for the predictive maintenance of transmission lines, specifically targeting fault detection, failure prediction, and maintenance optimization. Synthetic data was used to simulate parameters such as current, voltage, and temperature. Data preprocessing techniques, including cleaning and normalization, were performed. A supervised learning approach, the Random Forest Classifier, was applied using Python to mimic real-world fault scenarios. Model performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.The findings demonstrate that AI-based predictive maintenance has the potential to improve power system reliability and efficiency by reducing downtime and optimizing maintenance scheduling. The study also addresses key challenges, such as data availability and model generalization, proposing solutions like data augmentation and hybrid model design. Ultimately, this research provides a framework for developing scalable, data-driven predictive maintenance systems, advancing smart grid
technologies and sustainable power system management.

Cloud computing has revolutionized the way businesses manage their IT infrastructure, offering scalable, cost-effective, and flexible solutions. However, as organizations migrate to the cloud, cybersecurity becomes a critical concern. This paper explores how cloud computing enhances cybersecurity for businesses by leveraging advanced security mechanisms such as encryption, multi-factor authentication, artificial intelligence (AI)-driven threat detection, and automated compliance management. Cloud service providers (CSPs) offer robust security frameworks, including real-time monitoring, distributed denial-of service (DDoS) protection, and secure access controls, reducing the risk of cyber threats. Additionally, cloud-based security facilitates disaster recovery, data loss prevention, and regulatory compliance, strengthening overall business resilience. While cloud computing introduces new security challenges, implementing best practices and leveraging CSP security measures can significantly enhance an organization's cybersecurity posture. This study highlights the benefits, challenges, and future trends of cloud computing in
securing business operations against evolving cyber threats. To this purpose, this project designs and implements an encryption and multi-factor authentication system for cloud computing environments using a two-factor authentication approach: first-factor authentication via user ID/email and password, and second-factor authentication via OTP sent to user email.
The system is developed using HTML, CSS, JavaScript, and VueJS for the front-end, Laravel and PHP for the backend, and MySQL for the database.

This research explores the application of Artificial Intelligence (AI) and Machine Learning (ML) for the predictive maintenance of transmission lines, specifically targeting fault detection, failure prediction, and maintenance optimization.
Synthetic data was used to simulate parameters such as current, voltage, and temperature. Data preprocessing techniques, including cleaning and normalization, were performed. A supervised learning approach, the Random Forest Classifier, was
applied using Python to mimic real-world fault scenarios. Model performance was evaluated using standard metrics: accuracy, precision, recall, and F1-score.The findings demonstrate that AI-based predictive maintenance has the potential to improve power system reliability and efficiency by reducing downtime and optimizing maintenance scheduling. The study also addresses key challenges, such as data availability and model generalization, proposing solutions like data augmentation and
hybrid model design. Ultimately, this research provides a framework for developing scalable, data-driven predictive maintenance systems, advancing smart grid technologies and sustainable power system management

The board objective of this study is to examine the role of artificial intelligence on firm financial performance. Specifically, this study investigated the effects chatbot applications, robotic process automation and AI application on firm financial performance. The study used a primary data collected from 50 employees of the commercial banks within Ugbowo, Benin city, Edo State. Various statistical and econometric tool were applied to analyze the data. The results revealed that chatbot applications have a positive and statistically significant impact on organization performance. Robotic process automation and AI application in decision making have a positive but statistically insignificant impact on organization performance Based on the findings, the study recommended that businesses should consider increasing their investment in chatbot technologies, Organizations should reassess the effectiveness of their RPA strategies and business should explore other AI areas like predictive analytics, customer insights, or process automation

Artificial Intelligence has no doubt come to stay so much that it is incongruous to emergence of future world without it. The Impact of Artificial Intelligence (AI) is felt inalmost, if not all fields of human endeavours including medicine, engineering, etc.The downside of this remarkable innovation is that, if left unchecked, it might constitute serious danger to, not only mankind but the world generally.Through the hermeneutic and critical analysis method this study shall define its concepts and point the implications of AI Artificial Intelligence. It shall therefore argue subsequently that in spite of the enormous advantages of Artificial Intelligence, concerted efforts must be taken to checkmate its excesses.This shall be done through the lens of Aristotle’s virtueethics which is built on the foundation of moderation.

This study examined the influence of Artificial Intelligence (AI) on knowledge acquisitionamongstudents of the University of Benin. The study was guided by four specific objectives, whichsought to determine the extent of students’ awareness and utilization of AI tools, assess theimpact of AI on students’ learning outcomes, identify the challenges associated with AI useinacademic activities, and explore students’ perceptions of AI as a tool for enhancing knowledgeacquisition. The study adopted a descriptive survey research design. A total of 200 respondentswere selected from various faculties within the University of Benin through a stratifiedrandomsampling technique. Data were collected using a structured questionnaire and analyzedusingdescriptive and inferential statistical tools. Findings revealed that a majority of the studentswere aware of and frequently used AI tools such as ChatGPT, Grammarly, and Google Bardforlearning and research purposes. The results further indicated that AI significantly enhancesstudents’ ability to access information, improve comprehension, and develop critical thinkingskills. However, challenges such as limited technical skills, unreliable internet access, andfearof academic dishonesty were identified as barriers to ef ective AI integration. The studyconcluded that AI serves as a powerful catalyst for knowledge acquisition when properly utilizedand recommended that the university provide training programs, stable internet facilities, andclear policies to guide the ethical use of AI in learning

The study investigated the influence of artificial intelligence platforms on students’ attitude towards learning in tertiary institutions in Edo State. The main purpose of the study was to examine how AI platforms affect students’ cognitive, emotional, behavioural attitudes, and overall academic behaviour. Data were collected using a structured instrument titled Influence of Artificial Intelligence Platforms on Students’ Attitude Towards Learning Questionnaire (IAPSALQ). A sample size of two hundred (200) students was selected from the University of Benin using a stratified random sampling technique. The study adopted a survey research design, and data were gathered through the administration of questionnaires to the selected respondents. The collected data were analyzed using mean, standard deviation, and percentages for proper interpretation. The findings revealed that artificial intelligence platforms such as ChatGPT, Quillbot, Grok, Turnitin, Copilot, and Duolingo help students think more clearly, understand difficult concepts, generate creative academic ideas, and improve the overall quality and originality of their academic work. The use of these platforms was also found to enhance students’ understanding, memory of key information, and general learning effectiveness. Furthermore, the study revealed that artificial intelligence platforms positively influence students’ emotional and behavioural attitudes towards learning. Students reported increased confidence, motivation, and support, as well as reduced frustration when using AI tools for academic tasks